{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据和简单的数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": ".. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n                \n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher's paper. Note that it's the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher's paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n     Mathematical Statistics\" (John Wiley, NY, 1950).\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...\n"
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n       [5. , 3.6, 1.4, 0.2],\n       [5.4, 3.9, 1.7, 0.4],\n       [4.6, 3.4, 1.4, 0.3],\n       [5. , 3.4, 1.5, 0.2],\n       [4.4, 2.9, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5.4, 3.7, 1.5, 0.2],\n       [4.8, 3.4, 1.6, 0.2],\n       [4.8, 3. , 1.4, 0.1],\n       [4.3, 3. , 1.1, 0.1],\n       [5.8, 4. , 1.2, 0.2],\n       [5.7, 4.4, 1.5, 0.4],\n       [5.4, 3.9, 1.3, 0.4],\n       [5.1, 3.5, 1.4, 0.3],\n       [5.7, 3.8, 1.7, 0.3],\n       [5.1, 3.8, 1.5, 0.3],\n       [5.4, 3.4, 1.7, 0.2],\n       [5.1, 3.7, 1.5, 0.4],\n       [4.6, 3.6, 1. , 0.2],\n       [5.1, 3.3, 1.7, 0.5],\n       [4.8, 3.4, 1.9, 0.2],\n       [5. , 3. , 1.6, 0.2],\n       [5. , 3.4, 1.6, 0.4],\n       [5.2, 3.5, 1.5, 0.2],\n       [5.2, 3.4, 1.4, 0.2],\n       [4.7, 3.2, 1.6, 0.2],\n       [4.8, 3.1, 1.6, 0.2],\n       [5.4, 3.4, 1.5, 0.4],\n       [5.2, 4.1, 1.5, 0.1],\n       [5.5, 4.2, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.2],\n       [5. , 3.2, 1.2, 0.2],\n       [5.5, 3.5, 1.3, 0.2],\n       [4.9, 3.6, 1.4, 0.1],\n       [4.4, 3. , 1.3, 0.2],\n       [5.1, 3.4, 1.5, 0.2],\n       [5. , 3.5, 1.3, 0.3],\n       [4.5, 2.3, 1.3, 0.3],\n       [4.4, 3.2, 1.3, 0.2],\n       [5. , 3.5, 1.6, 0.6],\n       [5.1, 3.8, 1.9, 0.4],\n       [4.8, 3. , 1.4, 0.3],\n       [5.1, 3.8, 1.6, 0.2],\n       [4.6, 3.2, 1.4, 0.2],\n       [5.3, 3.7, 1.5, 0.2],\n       [5. , 3.3, 1.4, 0.2],\n       [7. , 3.2, 4.7, 1.4],\n       [6.4, 3.2, 4.5, 1.5],\n       [6.9, 3.1, 4.9, 1.5],\n       [5.5, 2.3, 4. , 1.3],\n       [6.5, 2.8, 4.6, 1.5],\n       [5.7, 2.8, 4.5, 1.3],\n       [6.3, 3.3, 4.7, 1.6],\n       [4.9, 2.4, 3.3, 1. ],\n       [6.6, 2.9, 4.6, 1.3],\n       [5.2, 2.7, 3.9, 1.4],\n       [5. , 2. , 3.5, 1. ],\n       [5.9, 3. , 4.2, 1.5],\n       [6. , 2.2, 4. , 1. ],\n       [6.1, 2.9, 4.7, 1.4],\n       [5.6, 2.9, 3.6, 1.3],\n       [6.7, 3.1, 4.4, 1.4],\n       [5.6, 3. , 4.5, 1.5],\n       [5.8, 2.7, 4.1, 1. ],\n       [6.2, 2.2, 4.5, 1.5],\n       [5.6, 2.5, 3.9, 1.1],\n       [5.9, 3.2, 4.8, 1.8],\n       [6.1, 2.8, 4. , 1.3],\n       [6.3, 2.5, 4.9, 1.5],\n       [6.1, 2.8, 4.7, 1.2],\n       [6.4, 2.9, 4.3, 1.3],\n       [6.6, 3. , 4.4, 1.4],\n       [6.8, 2.8, 4.8, 1.4],\n       [6.7, 3. , 5. , 1.7],\n       [6. , 2.9, 4.5, 1.5],\n       [5.7, 2.6, 3.5, 1. ],\n       [5.5, 2.4, 3.8, 1.1],\n       [5.5, 2.4, 3.7, 1. ],\n       [5.8, 2.7, 3.9, 1.2],\n       [6. , 2.7, 5.1, 1.6],\n       [5.4, 3. , 4.5, 1.5],\n       [6. , 3.4, 4.5, 1.6],\n       [6.7, 3.1, 4.7, 1.5],\n       [6.3, 2.3, 4.4, 1.3],\n       [5.6, 3. , 4.1, 1.3],\n       [5.5, 2.5, 4. , 1.3],\n       [5.5, 2.6, 4.4, 1.2],\n       [6.1, 3. , 4.6, 1.4],\n       [5.8, 2.6, 4. , 1.2],\n       [5. , 2.3, 3.3, 1. ],\n       [5.6, 2.7, 4.2, 1.3],\n       [5.7, 3. , 4.2, 1.2],\n       [5.7, 2.9, 4.2, 1.3],\n       [6.2, 2.9, 4.3, 1.3],\n       [5.1, 2.5, 3. , 1.1],\n       [5.7, 2.8, 4.1, 1.3],\n       [6.3, 3.3, 6. , 2.5],\n       [5.8, 2.7, 5.1, 1.9],\n       [7.1, 3. , 5.9, 2.1],\n       [6.3, 2.9, 5.6, 1.8],\n       [6.5, 3. , 5.8, 2.2],\n       [7.6, 3. , 6.6, 2.1],\n       [4.9, 2.5, 4.5, 1.7],\n       [7.3, 2.9, 6.3, 1.8],\n       [6.7, 2.5, 5.8, 1.8],\n       [7.2, 3.6, 6.1, 2.5],\n       [6.5, 3.2, 5.1, 2. ],\n       [6.4, 2.7, 5.3, 1.9],\n       [6.8, 3. , 5.5, 2.1],\n       [5.7, 2.5, 5. , 2. ],\n       [5.8, 2.8, 5.1, 2.4],\n       [6.4, 3.2, 5.3, 2.3],\n       [6.5, 3. , 5.5, 1.8],\n       [7.7, 3.8, 6.7, 2.2],\n       [7.7, 2.6, 6.9, 2.3],\n       [6. , 2.2, 5. , 1.5],\n       [6.9, 3.2, 5.7, 2.3],\n       [5.6, 2.8, 4.9, 2. ],\n       [7.7, 2.8, 6.7, 2. ],\n       [6.3, 2.7, 4.9, 1.8],\n       [6.7, 3.3, 5.7, 2.1],\n       [7.2, 3.2, 6. , 1.8],\n       [6.2, 2.8, 4.8, 1.8],\n       [6.1, 3. , 4.9, 1.8],\n       [6.4, 2.8, 5.6, 2.1],\n       [7.2, 3. , 5.8, 1.6],\n       [7.4, 2.8, 6.1, 1.9],\n       [7.9, 3.8, 6.4, 2. ],\n       [6.4, 2.8, 5.6, 2.2],\n       [6.3, 2.8, 5.1, 1.5],\n       [6.1, 2.6, 5.6, 1.4],\n       [7.7, 3. , 6.1, 2.3],\n       [6.3, 3.4, 5.6, 2.4],\n       [6.4, 3.1, 5.5, 1.8],\n       [6. , 3. , 4.8, 1.8],\n       [6.9, 3.1, 5.4, 2.1],\n       [6.7, 3.1, 5.6, 2.4],\n       [6.9, 3.1, 5.1, 2.3],\n       [5.8, 2.7, 5.1, 1.9],\n       [6.8, 3.2, 5.9, 2.3],\n       [6.7, 3.3, 5.7, 2.5],\n       [6.7, 3. , 5.2, 2.3],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]])"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(150, 4)"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['sepal length (cm)',\n 'sepal width (cm)',\n 'petal length (cm)',\n 'petal width (cm)']"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(150,)"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "iris.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(150, 2)"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1],color=\"red\", marker='o')\n",
    "plt.scatter(X[y==1,0], X[y==1,1],color=\"blue\",marker='+')\n",
    "plt.scatter(X[y==2,0], X[y==2,1],color=\"green\",marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1],color=\"red\", marker='o')\n",
    "plt.scatter(X[y==1,0], X[y==1,1],color=\"blue\",marker='+')\n",
    "plt.scatter(X[y==2,0], X[y==2,1],color=\"green\",marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}